Return the number of days from startdate to enddate: datediff(‘2009-03-01’, ‘2009-02-27’) = 2

string

date_add(string startdate, int days)

Add a number of days to startdate: date_add(‘2008-12-31’, 1) = ‘2009-01-01’

string

date_sub(string startdate, int days)

Subtract a number of days to startdate: date_sub(‘2008-12-31’, 1) = ‘2008-12-30’

timestamp

from_utc_timestamp(timestamp, string timezone)

Assumes given timestamp ist UTC and converts to given timezone (as of Hive 0.8.0)

timestamp

to_utc_timestamp(timestamp, string timezone)

Assumes given timestamp is in given timezone and converts to UTC (as of Hive 0.8.0)

Mathematical Functions

The following built-in mathematical functions are supported in hive; most return NULL when the argument(s) are NULL:

Return Type

Name(Signature)

Example

BIGINT

round(double a)

Returns the rounded BIGINT value of the double

DOUBLE

round(double a, int d)

Returns the double rounded to d decimal places

BIGINT

floor(double a)

Returns the maximum BIGINT value that is equal or less than the double

BIGINT

ceil(double a), ceiling(double a)

Returns the minimum BIGINT value that is equal or greater than the double

double

rand(), rand(int seed)

Returns a random number (that changes from row to row) that is distributed uniformly from 0 to 1. Specifiying the seed will make sure the generated random number sequence is deterministic.

double

exp(double a)

Returns ea where e is the base of the natural logarithm

double

ln(double a)

Returns the natural logarithm of the argument

double

log10(double a)

Returns the base-10 logarithm of the argument

double

log2(double a)

Returns the base-2 logarithm of the argument

double

log(double base, double a)

Return the base “base” logarithm of the argument

double

pow(double a, double p), power(double a, double p)

Return ap

double

sqrt(double a)

Returns the square root of a

string

bin(BIGINT a)

Returns the number in binary format

string

hex(BIGINT a) hex(string a)

If the argument is an int, hex returns the number as a string in hex format. Otherwise if the number is a string, it converts each character into its hex representation and returns the resulting string.

string

unhex(string a)

Inverse of hex. Interprets each pair of characters as a hexidecimal number and converts to the character represented by the number.

Returns the string or bytes resulting from concatenating the strings or bytes passed in as parameters in order. e.g. concat(‘foo’, ‘bar’) results in ‘foobar’. Note that this function can take any number of input strings.

array<struct<string,double>>

context_ngrams(array<array>, array, int K, int pf)

Returns the top-k contextual N-grams from a set of tokenized sentences, given a string of “context”. See StatisticsAndDataMining for more information.

string

concat_ws(string SEP, string A, string B…)

Like concat() above, but with custom separator SEP.

string

concat_ws(string SEP, array)

Like concat_ws() above, but taking an array of strings. (as of Hive 0.9.0)

int

find_in_set(string str, string strList)

Returns the first occurance of str in strList where strList is a comma-delimited string. Returns null if either argument is null. Returns 0 if the first argument contains any commas. e.g. find_in_set(‘ab’, ‘abc,b,ab,c,def’) returns 3

string

format_number(number x, int d)

Formats the number X to a format like ‘#,###,###.##’, rounded to D decimal places, and returns the result as a string. If D is 0, the result has no decimal point or fractional part. (as of Hive 0.10.0)

string

get_json_object(string json_string, string path)

Extract json object from a json string based on json path specified, and return json string of the extracted json object. It will return null if the input json string is invalid.NOTE: The json path can only have the characters [0-9a-z_], i.e., no upper-case or special characters. Also, the keys *cannot start with numbers.* This is due to restrictions on Hive column names.

boolean

in_file(string str, string filename)

Returns true if the string str appears as an entire line in filename.

int

instr(string str, string substr)

Returns the position of the first occurence of substr in str

int

length(string A)

Returns the length of the string

int

locate(string substr, string str[, int pos])

Returns the position of the first occurrence of substr in str after position pos

string

lower(string A) lcase(string A)

string

lpad(string str, int len, string pad)

Returns str, left-padded with pad to a length of len

string

ltrim(string A)

Returns the string resulting from trimming spaces from the beginning(left hand side) of A e.g. ltrim(‘ foobar ‘) results in ‘foobar ‘

array<struct<string,double>>

ngrams(array<array >, int N, int K, int pf)

Returns the top-k N-grams from a set of tokenized sentences, such as those returned by the sentences() UDAF. See StatisticsAndDataMining for more information.

Returns the specified part from the URL. Valid values for partToExtract include HOST, PATH, QUERY, REF, PROTOCOL, AUTHORITY, FILE, and USERINFO. e.g. parse_url(‘http://facebook.com/path1/p.php?k1=v1&k2=v2#Ref1’, ‘HOST’) returns ‘facebook.com’. Also a value of a particular key in QUERY can be extracted by providing the key as the third argument, e.g. parse_url(‘http://facebook.com/path1/p.php?k1=v1&k2=v2#Ref1’, ‘QUERY’, ‘k1’) returns ‘v1’.

string

printf(String format, Obj… args)

Returns the input formatted according do printf-style format strings (as of Hive 0.9.0)

string

regexp_extract(string subject, string pattern, int index)

Returns the string extracted using the pattern. e.g. regexp_extract(‘foothebar’, ‘foo(.*?)(bar)’, 2) returns ‘bar.’ Note that some care is necessary in using predefined character classes: using ‘\s’ as the second argument will match the letter s; ‘s’ is necessary to match whitespace, etc. The ‘index’ parameter is the Java regex Matcher group() method index. See docs/api/java/util/regex/Matcher.html for more information on the ‘index’ or Java regex group() method.

Returns the string resulting from replacing all substrings in INITIAL_STRING that match the java regular expression syntax defined in PATTERN with instances of REPLACEMENT, e.g. regexp_replace(“foobar”, “oo|ar”, “”) returns ‘fb.’ Note that some care is necessary in using predefined character classes: using ‘\s’ as the second argument will match the letter s; ‘s’ is necessary to match whitespace, etc.

string

repeat(string str, int n)

Repeat str n times

string

reverse(string A)

Returns the reversed string

string

rpad(string str, int len, string pad)

Returns str, right-padded with pad to a length of len

string

rtrim(string A)

Returns the string resulting from trimming spaces from the end(right hand side) of A e.g. rtrim(‘ foobar ‘) results in ‘ foobar’

array<array>

sentences(string str, string lang, string locale)

Tokenizes a string of natural language text into words and sentences, where each sentence is broken at the appropriate sentence boundary and returned as an array of words. The ‘lang’ and ‘locale’ are optional arguments. e.g. sentences(‘Hello there! How are you?’) returns ( (“Hello”, “there”), (“How”, “are”, “you”) )

string

space(int n)

Return a string of n spaces

array

split(string str, string pat)

Split str around pat (pat is a regular expression)

map<string,string>

str_to_map(text[, delimiter1, delimiter2])

Splits text into key-value pairs using two delimiters. Delimiter1 separates text into K-V pairs, and Delimiter2 splits each K-V pair. Default delimiters are ‘,’ for delimiter1 and ‘=’ for delimiter2.

Returns the substring or slice of the byte array of A starting from start position with length len e.g. substr(‘foobar’, 4, 1) results in ‘b’

string

translate(string input, string from, string to)

Translates the input string by replacing the characters present in the from string with the corresponding characters in the to string. This is similar to the translatefunction in PostgreSQL. If any of the parameters to this UDF are NULL, the result is NULL as well (available as of Hive 0.10.0)

string

trim(string A)

Returns the string resulting from trimming spaces from both ends of A e.g. trim(‘ foobar ‘) results in ‘foobar’

string

upper(string A) ucase(string A)

Returns the string resulting from converting all characters of A to upper case e.g. upper(‘fOoBaR’) results in ‘FOOBAR’

Collection Functions

The following built-in collection functions are supported in hive:

Return Type

Name(Signature)

Example

int

size(Map)

Returns the number of elements in the map type

int

size(Array)

Returns the number of elements in the array type

array

map_keys(Map)

Returns an unordered array containing the keys of the input map

array

map_values(Map)

Returns an unordered array containing the values of the input map

boolean

array_contains(Array, value)

Returns TRUE if the array contains value

array

sort_array(Array)

Sorts the input array in ascending order according to the natural ordering of the array elements and returns it (as of version 0.9.0)

Built-in Aggregate Functions (UDAF)

The following are built-in aggregate functions are supported in Hive:

Return Type

Name(Signature)

Example

bigint

count(*), count(expr), count(DISTINCT expr[, expr_.])

count(*) – Returns the total number of retrieved rows, including rows containing NULL values; count(expr) – Returns the number of rows for which the supplied expression is non-NULL; count(DISTINCT expr[, expr]) – Returns the number of rows for which the supplied expression(s) are unique and non-NULL.

double

sum(col), sum(DISTINCT col)

Returns the sum of the elements in the group or the sum of the distinct values of the column in the group

double

avg(col), avg(DISTINCT col)

Returns the average of the elements in the group or the average of the distinct values of the column in the group

double

min(col)

Returns the minimum of the column in the group

double

max(col)

Returns the maximum value of the column in the group

double

variance(col), var_pop(col)

Returns the variance of a numeric column in the group

double

var_samp(col)

Returns the unbiased sample variance of a numeric column in the group

double

stddev_pop(col)

Returns the standard deviation of a numeric column in the group

double

stddev_samp(col)

Returns the unbiased sample standard deviation of a numeric column in the group

double

covar_pop(col1, col2)

Returns the population covariance of a pair of numeric columns in the group

double

covar_samp(col1, col2)

Returns the sample covariance of a pair of a numeric columns in the group

double

corr(col1, col2)

Returns the Pearson coefficient of correlation of a pair of a numeric columns in the group

double

percentile(BIGINT col, p)

Returns the exact pth percentile of a column in the group (does not work with floating point types). p must be between 0 and 1. NOTE: A true percentile can only be computed for integer values. Use PERCENTILE_APPROX if your input is non-integral.

array

percentile(BIGINT col, array(p1 [, p2]…))

Returns the exact percentiles p1, p2, … of a column in the group (does not work with floating point types). pi must be between 0 and 1. NOTE: A true percentile can only be computed for integer values. Use PERCENTILE_APPROX if your input is non-integral.

double

percentile_approx(DOUBLE col, p [, B])

Returns an approximate pth percentile of a numeric column (including floating point types) in the group. The B parameter controls approximation accuracy at the cost of memory. Higher values yield better approximations, and the default is 10,000. When the number of distinct values in col is smaller than B, this gives an exact percentile value.

array

percentile_approx(DOUBLE col, array(p1 [, p2]…) [, B])

Same as above, but accepts and returns an array of percentile values instead of a single one.

array

histogram_numeric(col, b)

Computes a histogram of a numeric column in the group using b non-uniformly spaced bins. The output is an array of size b of double-valued (x,y) coordinates that represent the bin centers and heights

array

collect_set(col)

Returns a set of objects with duplicate elements eliminated

Built-in Table-Generating Functions (UDTF)

Normal user-defined functions, such as concat(), take in a single input row and output a single output row. In contrast, table-generating functions transform a single input row to multiple output rows.

Return Type

Name(Signature)

Example

inline(ARRAY<STRUCT[,STRUCT]>)

Explodes an array of structs into a table (as of Hive 0.10)

Explode

explode() takes in an array as an input and outputs the elements of the array as separate rows. UDTF’s can be used in the SELECT expression list and as a part of LATERAL VIEW.

Functions for Text Analytics

Return Type

Name(Signature)

Example

array<struct<string,double>>

context_ngrams(array<array>, array, int K, int pf)

Returns the top-k contextual N-grams from a set of tokenized sentences, given a string of “context”. See StatisticsAndDataMining for more information.N-grams are subsequences of length N drawn from a longer sequence. The purpose of the ngrams() UDAF is to find the k most frequent n-grams from one or more sequences. It can be used in conjunction with the sentences() UDF to analyze unstructured natural language text, or the collect() function to analyze more general string data.

array<struct<string,double>>

ngrams(array<array>, int N, int K, int pf)

Returns the top-k N-grams from a set of tokenized sentences, such as those returned by the sentences() UDAF. See StatisticsAndDataMining for more information.Contextual n-grams are similar to n-grams, but allow you to specify a ‘context’ string around which n-grams are to be estimated. For example, you can specify that you’re only interested in finding the most common two-word phrases in text that follow the context “I love”. You could achieve the same result by manually stripping sentences of non-contextual content and then passing them to ngrams(), but context_ngrams() makes it much easier.

Prakash Janakiraman, Co-Founder and VP Engineering

Qubole is a significantly more polished product than EMR. Data scientists can explore their data in S3, create tables and query those tables all via an easy-to-use web UI

Yali Sassoon, Co-founder

Snowplow Analytics

Qubole’s fantastic support has been key in our successful deployment. They continue to deliver of new features and revisit the ones that we ask for

Joris Spermon, VP Tech & Development

YD World

Our goal at MediaMath was to take our existing industry leading infrastructure to the next level handling new complex analytics tasks. Qubole has helped us enable this goal with minimal risk.

Marc Rossen, Sr. Director Data and Analytics

MediaMath

Instead of worrying about provisioning clusters of machines or job flows or whatever, Qubole lets you focus on your data and your queries … The Qubole guys have been extremely helpful!

Nicholas Andonakis, Senior Product Analyst

BigCommerce

The service spins up users’ clusters only when a job is started, then automatically scales or contracts them based on the workload, and spins the servers down once the job is done.

Derrick Harris, Senior Writer

GigaOM

Qubole’s Hadoop and Hive interfaces are vastly superior to the default CLIs, which scare business analysts and hinder meaningful analyses of the gaming logs that we collect. With Qubole, business analysts are self-sufficient in using a Big Data platform to meet their advanced analytic needs.

Senior Director, Game Dev Ops and Analytics

Online Gaming Company

top-performing technologies in the data industry are definitely taking aim at democratizing data tools and bringing the power of data to smaller businesses. This is a major change in the data industry, and Qubole Data Service is a great example

Geoff Domoracki, Founder and CEO

DataWeek

I’m very happy to be using Qubole in production. Qubole has saved me a lot of time, effort, and trouble in getting my data processing pipelines up and running. My data pipelines process Appnexus data in Amazon S3 which is then stored in Vertica. The engineering team understands the complexities and provided awesome support!

Chief Engineer

Real-time Ads Retargeting Startup

There’s a whole world of web companies, SMBs and other non-Facebooks or Yahoos that will want to use Hadoop but not want to run it in-house…offering a cloud service makes it easier for these users to get started with the platform and for Qubole to keep improving.

Derrick Harris, Senior Writer

GigaOM

Qubole offers a big data ETL and exploration service through auto-scaling Hadoop clusters with a web user interface for data exploration and integration with various data sources. The service can do (nearly) everything EMR can do, and it goes further

Christian Prokopp, Contributor

George Chow, CTO

Simba Technologies

“The integration of Tableau and Qubole makes it faster and easier for our customers to operationalize Big Data…lowers the resource barriers to deriving the benefits of Big Data because customers can deploy our joint solution seamlessly and cost effectively.”